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Discovering User’s Trends and Routines from Location Based Social Networks

1
Axpe Consulting Cantabria S.L., 39600 Camargo, Spain
2
Departamento de Matemáticas, Estadística y Computación, Universidad de Cantabria, 39005 Santander, Spain
*
Author to whom correspondence should be addressed.
Presented at the 12th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, 4–7 December 2018.
Proceedings 2018, 2(19), 1222; https://doi.org/10.3390/proceedings2191222
Published: 30 October 2018
(This article belongs to the Proceedings of UCAmI 2018)
Location data is a powerful source of information to discover user’s trends and routines. A suitable identification of the user context can be exploited to provide automatically services adapted to the user preferences. In this paper, we define a Dynamic Bayesian Network model and propose a method that processes location annotated data in order to train the model. Finally, our model enables us to predict future location contexts from the user patterns. A case study evaluates the proposal using real-world data of a location-based social network.
Keywords: location based social networks; user modeling; probabilistic graphical models; geolocation location based social networks; user modeling; probabilistic graphical models; geolocation
MDPI and ACS Style

Salomón, S.; Duque, R.; Montaña, J.L. Discovering User’s Trends and Routines from Location Based Social Networks. Proceedings 2018, 2, 1222.

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